FCC-Data-Analysis-Certification

This repository contains a collection of the relevant coursework I completed to earn the Data Analysis with Python certification from freeCodeCamp.org. You can verify the certification here.

If you want a strong sense of my fluency in Python and how I communicate via comments in the context of code development, each project folder contains a notebook file (identified by the *.ipynb extension) for that purpose. In addition, copies of the files I submitted to earn the certification are also present in each project folder, and are named using the actual project title (for example, Project 1's submission file is mean_var_std.py). The fundamental Python code is the same between the submission files and their respective notebook presentation, aside from a few test cases and/or comments.

As a result of working on this certification, I gained proficiency with the following Python modules:

• Pandas
• Matplotlib
• Seaborn
• Numpy

Example work:

Project 4 ("Time Series Visualizer")

Note: two variables used below were declared in the global scope: "df", which is the DataFrame holding the time series data, and "month_labels", which is a list of strings representing full month names.

import pandas as pd
import seaborn as sns
import matplotlib as mpl

def draw_bar_plot():

    # Copy and modify data for monthly bar plot
    # Extract months and years to individual columns with a lambda
    # retitle the columns so they'll match the project requirements
    df_bar = pd.DataFrame(
        data={
            'Years': df.index.to_series().apply(lambda t: t.year),
            'Month': df.index.to_series().apply(lambda t: t.month),
            'Average Page Views': df['value']
        }
    )

    # use groupby to select each unique month/year combination, returning the mean.
    # reset the index so we don't confuse ourselves or our plotting backend.
    df_bar = df_bar.groupby(['Month', 'Years']).mean().reset_index()

    # now that everything is grouped and we have the mean for the pageviews,
    # set the values for the "Month" column to actual month names.
    df_bar['Month'] = df_bar['Month'].apply(lambda t: month_labels[int(t) - 1])

    # Use seaborn to draw the plot.
    fig = sns.catplot(
        data=df_bar,
        x='Years',
        y='Average Page Views',
        kind='bar',
        hue='Month',
        legend_out=False
    )

    # Need to set the return value to "fig.fig" because we used a seaborn figure-level function,
    # And the actual matplotlib figure is a property of the returned object.
    fig = fig.fig
    
    fig.savefig('bar_plot.png')
    return fig
    

And the output:

Project 5 (Sea Level Predictor):

This plot shows two linear regression lines on a scatter plot of sea level measurements taken over the course of a century.